Contingency Planning for TFM Decision Support

As the National Airspace System (NAS) evolves into a more automated system, it will be essential that human operators can effectively team with their automated Decision Support Systems (DSS) to manage the performance of the system. When automated systems recommend courses of action, the human operator must understand the operational recommendations with sufficient depth and clarity to evaluate their appropriateness and monitor the performance of the system. In this proposed effort, we will conduct specific research in human-autonomy teaming in the context of Traffic Flow Management (TFM) DSSs.In this proposed research effort, Mosaic ATM will address this underlying cause of the lack of trust on the part of the human specialist in ATM DSSs by developing the ability for ATM DSSs to explicitly identify backup plans associated with the primary recommended course of action. By providing an explicit backup plan, the ATM DSS will better align with the humanrsquo;s approach to the operational situation and will be more transparent in its function. The human user will gain trust in the automation by seeing deeper into the DSSrsquo;s evaluation process, knowing that the automation has a backup plan at the ready in case its primary recommendation evolves into an unsatisfactory state.In this research, we will address the deep and fascinating algorithmic and human factors research topics to align ATM DSS information displays and even their lsquo;way of thinkingrsquo; with the human TFM specialist. Algorithmically, we will study how to define the backup plans and associate a plan B with a plan A. How do we compute the amount of time that is available for switching to the backup plan? From the human factors perspective, how do we convey the information to the TMC so that the recommendations are trusted and accepted?